OpenRaiser/Intern-Atlas huggingface dataset:
Dirty Bits in Low-Earth Orbit: The Carbon Footprint of Launching Computers
Venue: arXiv cs.CY 2026
Year: 2026

Low-Earth Orbit (LEO) satellites are increasingly proposed for communication and in-orbit computing, achieving low-latency global services. However, their sustainability remains largely unexamined. This paper investigates the carbon footprint of computing in space, focusing on lifecycle emissions from launch over orbital operation to re-entry. We present ESpaS, a lightweight tool for estimating carbon intensities across CPU usage, memory, and networking in orbital vs. terrestrial settings. Three worked examples compare (i) launch technologies (state-of-the-art rocket vs. potential next generation), (ii) operational emissions of data center workloads in orbit and on the ground and, (iii) in-orbit aggregation with raw data transmission. Results show that, even under optimistic assumptions, in-orbit systems incur significantly higher carbon costs - primarily due to embodied emissions from launch and re-entry. Our findings advocate for carbon-aware design principles and regulatory oversight in developing sustainable digital infrastructure in orbit.
Matrix-Driven Identification and Reconstruction of LLM Weight Homology
Venue: arXiv cs.CL 2026
Year: 2026

We propose Matrix-Driven Identification and Reconstruction (MDIR), a SOTA large language model homology method that accurately detects weight correspondences between models and provides rigorous $p$-value estimation of the statistical significance of these correspondences. Our method does not require model inference, and allows the detection of unattributed reuse or replication of model weights even on low-resource devices as it compares only a single pair of matrices at a time. We leverage matrix analysis, polar decomposition, and Large Deviation Theory (LDT) to achieve accurate reconstruction of weight relationships between models. Notably, MDIR is the first method to achieve perfect scores on both Area-Under-Curve (AUC) and accuracy metrics across different source models on LeaFBench.
HapticLLaMA: A Multimodal Sensory Language Model for Haptic Captioning
Venue: arXiv cs.CL 2026
Year: 2026

Haptic captioning is the task of generating natural language descriptions from haptic signals, such as vibrations, for use in virtual reality, accessibility, and rehabilitation applications. While previous multimodal research has focused primarily on vision and audio, haptic signals for the sense of touch remain underexplored. To address this gap, we formalize the haptic captioning task and propose HapticLLaMA, a multimodal sensory language model that interprets vibration signals into descriptions in a given sensory, emotional, or associative category. We investigate two types of haptic tokenizers, a frequency-based tokenizer and an EnCodec-based tokenizer, that convert haptic signals into sequences of discrete units, enabling their integration with the LLaMA model. HapticLLaMA is trained in two stages: (1) supervised fine-tuning using the LLaMA architecture with LoRA-based adaptation, and (2) fine-tuning via reinforcement learning from human feedback (RLHF). We assess HapticLLaMA's captioning performance using both automated n-gram metrics and human evaluation. HapticLLaMA demonstrates strong capability in interpreting haptic vibration signals, achieving a METEOR score of 59.98 and a BLEU-4 score of 32.06 respectively. Additionally, over 61% of the generated captions received human ratings above 3.5 on a 7-point scale, with RLHF yielding a 10% improvement in the overall rating distribution, indicating stronger alignment with human haptic perception. These findings highlight the potential of large language models to process and adapt to sensory data.
BigTokDetect: A Clinically-Informed Vision-Language Modeling Framework for Detecting Pro-Bigorexia Videos on TikTok
Venue: arXiv cs.CV 2026
Year: 2026

Social media platforms face escalating challenges in detecting harmful content that promotes muscle dysmorphic behaviors and cognitions (bigorexia). This content can evade moderation by camouflaging as legitimate fitness advice and disproportionately affects adolescent males. We address this challenge with BigTokDetect, a clinically informed framework for identifying pro-bigorexia content on TikTok. We introduce BigTok, the first expert-annotated multimodal benchmark dataset of over 2,200 TikTok videos labeled by clinical psychiatrists across five categories and eighteen fine-grained subcategories. Comprehensive evaluation of state-of-the-art vision-language models reveals that while commercial zero-shot models achieve the highest accuracy on broad primary categories, supervised fine-tuning enables smaller open-source models to perform better on fine-grained subcategory detection. Ablation studies show that multimodal fusion improves performance by 5 to 15 percent, with video features providing the most discriminative signals. These findings support a grounded moderation approach that automates detection of explicit harms while flagging ambiguous content for human review, and they establish a scalable framework for harm mitigation in emerging mental health domains.
Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model
Venue: arXiv cs.NI 2026
Year: 2026

With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (IDNs) address this by translating high-level intents into optimization policies. Large Language Models (LLMs) can enhance this process by understanding complex human instructions, enabling adaptive and intelligent automation. Given the rapid advancements in Generative AI (GenAI), a comprehensive survey of LLM-based IDN architectures in disaggregated Radio Access Network (RAN) environments is both timely and critical. This article provides such a survey, along with a case study on a selective State-Space Model (SSM)-enabled IDN architecture that integrates GenAI across three key stages: intent processing, intent validation, and intent execution. For the first time in the literature, we propose a hierarchical framework built on Mamba-SSM that introduces GenAI across all stages of the IDN pipeline. We further present a case study demonstrating that the proposed Mamba architecture significantly improves network performance through intelligent automation, surpassing existing IDN approaches. In a multi-cell 5G/6G scenario, the proposed architecture reduces quality of service drift by up to 70%, improves throughput by up to 80 Mbps, and lowers inference time to 60-70 ms, outperforming GenAI, reinforcement learning, and non-machine learning baselines.
Generalizing Scaling Laws for Dense and Sparse Large Language Models
Venue: arXiv cs.LG 2026
Year: 2026

Despite recent advancements of large language models (LLMs), optimally predicting the model size for LLM pretraining or allocating optimal resources still remains a challenge. Several efforts have addressed the challenge by proposing different empirical scaling laws, but almost all of them are architecture-specific (dense or sparse). In this work we revisit existing empirical scaling laws and propose a generalized scaling law to provide a unified framework that is applicable to both dense and sparse large language models. We evaluate and compare our proposed scaling law with existing scaling laws and demonstrate that our proposed scaling law captures the scaling behavior of existing scaling laws. Further, we show an IsoFLOP comparison between our proposed scaling law and the state-of-the-art scaling law to illustrate the effectiveness of our proposed scaling law for Mixture-of-Expert (MoE)-based very large LLMs like DeepSeek-V3. Our proposed scaling law can be used to estimate the best model hyperparameters (Model size, Tokens and Compute) for a given sparsity or to identify the optimal sparsity for the given model hyperparameters.
Quantifying the Generalization Gap: A New Benchmark for Out-of-Distribution Graph-Based Android Malware Classification
Venue: arXiv cs.CR 2026
Year: 2026

While graph-based Android malware classifiers achieve over 94% accuracy on standard benchmarks, they exhibit a significant generalization gap under distribution shift, suffering up to 45% performance degradation when encountering unseen malware variants from known families. This work systematically investigates this critical yet overlooked challenge for real-world deployment by introducing a benchmarking suite designed to simulate two prevalent scenarios: MalNet-Tiny-Common for covariate shift, and MalNet-Tiny-Distinct for domain shift. Furthermore, we identify an inherent limitation in existing benchmarks where the inputs are structure-only function call graphs, which fails to capture the latent semantic patterns necessary for robust generalization. To verify this, we construct a semantic enrichment framework that augments the original topology with function-level attributes, including lightweight metadata and LLM-based code embeddings. By providing this expanded feature set, we aim to equip future research with richer behavioral information to facilitate the development of more sophisticated detection techniques. Empirical evaluations confirm the effectiveness of our data-centric methodology, with which classification performs better under distribution shift compared to model-based approaches, and consistently further enhances robustness when used in conjunction. We release our precomputed datasets, along with an extensible implementation of our comprehensive pipeline, to lay the groundwork for building resilient malware detection systems for evolving threat environments.
GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimization
Venue: arXiv cs.AI 2026
Year: 2026

Local search is an important class of incomplete algorithms for solving Distributed Constraint Optimization Problems (DCOPs) but it often converges to poor local optima. While Generalized Distributed Breakout Algorithm (GDBA) provides a comprehensive rule set to escape premature convergence, its empirical benefits remain marginal on general-valued problems. In this work, we systematically examine GDBA and identify three factors that potentially lead to its inferior performance, i.e., over-aggressive constraint violation conditions, unbounded penalty accumulation, and uncoordinated penalty updates. To address these issues, we propose Distributed Guided Local Search (DGLS), a novel GLS framework for DCOPs that incorporates an adaptive violation condition to selectively penalize constraints with high cost, a penalty evaporation mechanism to control the magnitude of penalization, and a synchronization scheme for coordinated penalty updates. We theoretically show that the penalty values are bounded, and agents play a potential game in DGLS. Extensive empirical results on various benchmarks demonstrate the great superiority of DGLS over state-of-the-art baselines. Compared to Damped Max-sum with high damping factors, our DGLS achieves competitive performance on general-valued problems, and outperforms by significant margins on structured problems in terms of anytime results.
Frequency-Aware Graph Construction and Search for Dynamic Vector Databases
Venue: arXiv cs.DB 2026
Year: 2026

Approximate Nearest Neighbor Search (ANNS) is a crucial operation in databases and artificial intelligence. While graph-based ANNS methods like HNSW and NSG excel in performance, they assume uniform query distribution. However, in real-world scenarios, user preferences and temporal dynamics often result in certain data points being queried more frequently than others, and these query patterns can change over time. To better leverage such characteristics, we propose DQF, a novel Dual-Index Query Framework. This framework features a dual-layer index structure and a dynamic search strategy based on a decision tree. The dual-layer index includes a hot index for high-frequency nodes and a full index covering the entire dataset, allowing for the separate management of hot and cold queries. Furthermore, we propose a dynamic search strategy that employs a decision tree to determine whether a query is of the high-frequency type, avoiding unnecessary searches in the full index through early termination. Additionally, to address fluctuations in query frequency, we design an update mechanism to manage the hot index. New high-frequency nodes will be inserted into the hot index, which is periodically rebuilt when its size exceeds a predefined threshold, removing outdated low-frequency nodes. Experiments on four real-world datasets demonstrate that the Dual-Index Query Framework achieves a significant speedup of 2.0-5.7x over state-of-the-art algorithms while maintaining a 95% recall rate. Importantly, it avoids full index reconstruction even as query distributions change, underscoring its efficiency and practicality in dynamic query distribution scenarios.
CCFQA: A Benchmark for Cross-Lingual and Cross-Modal Speech and Text Factuality Evaluation
Venue: arXiv cs.CL 2026
Year: 2026

As Large Language Models (LLMs) are increasingly popularized in the multilingual world, ensuring hallucination-free factuality becomes markedly crucial. However, existing benchmarks for evaluating the reliability of Multimodal Large Language Models (MLLMs) predominantly focus on textual or visual modalities with a primary emphasis on English, which creates a gap in evaluation when processing multilingual input, especially in speech. To bridge this gap, we propose a novel Cross-lingual and Cross-modal Factuality benchmark (CCFQA). Specifically, the CCFQA benchmark contains parallel speech-text factual questions across 8 languages, designed to systematically evaluate MLLMs' cross-lingual and cross-modal factuality capabilities. Our experimental results demonstrate that current MLLMs still face substantial challenges on the CCFQA benchmark. Furthermore, we propose a few-shot transfer learning strategy that effectively transfers the Question Answering (QA) capabilities of LLMs in English to multilingual Spoken Question Answering (SQA) tasks, achieving competitive performance with GPT-4o-mini-Audio using just 5-shot training. We release CCFQA as a foundational research resource to promote the development of MLLMs with more robust and reliable speech understanding capabilities. Our code and dataset are available at https://github.com/yxduir/ccfqa.
Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability
Venue: arXiv cs.AI 2026
Year: 2026

Temporal Video Grounding (TVG) aims to localize video segments corresponding to a given textual query, which often describes human actions. However, we observe that current methods, usually optimizing for high temporal Intersection-over-Union (IoU), frequently struggle to accurately recognize or understand the underlying actions in both the video and query, thus reducing the effectiveness of these methods. To address this, we propose a novel TVG framework that integrates inversion-based TVG as auxiliary objectives to maintain the model's action understanding ability. We introduce three kinds of inversion TVG tasks derived from the original TVG annotations: (1) Verb Completion, predicting masked verbs (actions) in queries given video segments; (2) Action Recognition, identifying query-described actions; and (3) Video Description, generating descriptions containing query-relevant actions given video segments. These inversion tasks are entirely derived from the original TVG tasks and are probabilistically integrated with them within a reinforcement learning framework. By leveraging carefully designed reward functions, the model preserves its ability to understand actions, thereby improving the accuracy of temporal grounding. Experiments show our method outperforms state-of-the-art approaches, achieving a 7.1\% improvement in R1@0.7 on Charades-STA for a 3B model.
Data Selection for LLM Alignment Using Fine-Grained Preferences
Venue: arXiv cs.LG 2026
Year: 2026

Large language models (LLMs) alignment aims to ensure that the behavior of LLMs meets human preferences. While collecting data from multiple fine-grained, aspect-specific preferences becomes more and more feasible, existing alignment methods typically work on a single preference and thus struggle with conflicts inherent in such aggregated datasets. As one early attempt, in this paper, we propose a data-centric approach to align LLMs through the effective use of fine-grained preferences. Specifically, we formulate the problem as a direct fine-grained preference optimization and introduce preference divergence (PD) that quantifies inter-aspect preference conflicts. Instead of directly tackling the consequent complicated optimization, we recast it as a data selection problem and propose a simple yet effective strategy, which identifies a subset of data corresponding to the most negative PD values, for efficient training. We theoretically analyze the loss-bound optimality of our selection strategy and conduct extensive empirical studies on varied settings and datasets to demonstrate that our practical selection method could achieve consistent improvement against standard full-data alignment, using even just 30% of the data. Our work shares a line that LLM alignment using fine-grained preferences is highly feasible.
LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval
Venue: arXiv cs.CL 2026
Year: 2026

Tool learning has emerged as a promising paradigm for large language models (LLMs) to solve many real-world tasks. Nonetheless, with the tool repository rapidly expanding, it is impractical to contain all tools within the limited input length of LLMs. To alleviate these issues, researchers have explored incorporating a tool retrieval module to select the most relevant tools or represent tools as unique tokens within LLM parameters. However, most state-of-the-art methods are under transductive settings, assuming all tools have been observed during training. Such a setting deviates from reality as the real-world tool repository is evolving and incorporates new tools frequently. When dealing with these unseen tools, which refer to tools not encountered during the training phase, these methods are limited by two key issues, including the large distribution shift and the vulnerability of similarity-based retrieval. To this end, inspired by human cognitive processes of mastering unseen tools through discovering and applying the logical information from prior experience, we introduce a novel Logic-Guided Semantic Bridging framework for inductive tool retrieval, namely, LoSemB, which aims to mine and transfer latent logical information for inductive tool retrieval without costly retraining. Specifically, LoSemB contains a logic-based embedding alignment module to mitigate distribution shifts and implements a relational augmented retrieval mechanism to reduce the vulnerability of similarity-based retrieval. Extensive experiments demonstrate that LoSemB achieves advanced performance in inductive settings while maintaining desirable effectiveness in the transductive setting.
Heterogeneity in Entity Matching: A Survey and Experimental Analysis
Venue: arXiv cs.DB 2026
Year: 2026

Entity matching (EM) is a fundamental task in data integration and analytics, essential for identifying records that refer to the same real-world entity across diverse sources. In practice, datasets often differ widely in structure, format, schema, and semantics, creating substantial challenges for EM. We refer to this setting as Heterogeneous EM (HEM). This survey offers a unified perspective on HEM by introducing a taxonomy, grounded in prior work, that distinguishes two primary categories -- representation and semantic heterogeneity -- and their subtypes. The taxonomy provides a systematic lens for understanding how variations in data form and meaning shape the complexity of matching tasks. We then connect this framework to the FAIR principles -- Findability, Accessibility, Interoperability, and Reusability -- demonstrating how they both reveal the challenges of HEM and suggest strategies for mitigating them. Building on this foundation, we critically review recent EM methods, examining their ability to address different heterogeneity types, and conduct targeted experiments on state-of-the-art models to evaluate their robustness and adaptability under semantic heterogeneity. Our analysis uncovers persistent limitations in current approaches and points to promising directions for future research, including multimodal matching, human-in-the-loop workflows, deeper integration with large language models and knowledge graphs, and fairness-aware evaluation in heterogeneous settings.
Exploring Safety Alignment Evaluation of LLMs in Chinese Mental Health Dialogues via LLM-as-Judge
Venue: arXiv cs.CL 2026
Year: 2026

Evaluating the safety alignment of LLM responses in high-risk mental health dialogues is particularly difficult due to missing gold-standard answers and the ethically sensitive nature of these interactions. To address this challenge, we propose PsyCrisis-Bench, a reference-free evaluation benchmark based on real-world Chinese mental health dialogues. It evaluates whether the model responses align with the safety principles defined by experts. Specifically designed for settings without standard references, our method adopts a prompt-based LLM-as-Judge approach that conducts in-context evaluation using expert-defined reasoning chains grounded in psychological intervention principles. We employ binary point-wise scoring across multiple safety dimensions to enhance the explainability and traceability of the evaluation. Additionally, we present a manually curated, high-quality Chinese-language dataset covering self-harm, suicidal ideation, and existential distress, derived from real-world online discourse. Experiments on 3600 judgments show that our method achieves the highest agreement with expert assessments and produces more interpretable evaluation rationales compared to existing approaches. Our dataset and evaluation tool are publicly available to facilitate further research.
Experimental Validation of Provably Covert Communication Using Software-Defined Radio
Venue: arXiv cs.NI 2026
Year: 2026

The fundamental information-theoretic limits of covert, or low probability of detection/intercept (LPD/LPI), communication have been extensively studied for over a decade, resulting in the square root law (SRL): only $L\sqrt{n}$ covert bits can be reliably transmitted over time-bandwidth product $n$, for constant $L>0$. Transmitting more either results in detection or decoding errors. The SRL imposes significant constraints on hardware realization of mathematically-guaranteed covert communication. Indeed, they preclude using standard link maintenance operations that are taken for granted in non-covert communication. Thus, experimental validation of covert communication is underexplored: to date, only two experimental studies of SRL-based covert communication are available, both focusing on optical channels. Here, we report a demonstration of provably-secure covert radio-frequency (RF) communication using software-defined radios (SDRs). This validates theoretical predictions, opens practical avenues for implementing covert communication systems, and raises further research questions.